Andreas Veithen's blog

Sunday, November 2, 2014

This article describes how to run WebSphere Application Server in a Docker container. We are going to use the developer version to create a full profile, but the instructions can easily be adapted to a regular WebSphere version (provided you have an appropriate license). To create the Docker image, download IBM Installation Manager for Linux x86_64 and use the following Dockerfile, after replacing the -userName and -userPassword arguments with your IBM ID:

Note that by executing this Dockerfile you accept the license agreement for IBM Installation Manager and WebSphere Application Server for Developers.

Here are some more details about the Dockerfile:

Only IBM Installation Manager needs to be downloaded before creating the image. The product itself (WebSphere Application Server for Developers 8.5.5) is downloaded by Installation Manager during image creation. Note that this may take a while. The preserveDownloadedArtifacts=false preference instructs Installation Manager to remove the downloaded packages. This reduces the size of the image.

The Dockerfile creates a default application server profile that is configured to run as a non-root user. The HTTP port is 9080 and the URL of the admin console is http://...:9060/ibm/console. New containers should be created with the following options: -p 9060:9060 -p 9080:9080.

To see the WebSphere server logs, use the following command (requires Docker 1.3):

docker exec container_id tail -F /var/was/logs/server1/SystemOut.log

Docker assigns a new hostname to every newly created container. This is a problem because the serverindex.xml file in the configuration of the WebSphere profile contains the hostname. That is to say that WebSphere implicitly assumes that the hostname is static and not expected to change after the profile has been created. To overcome this problem the Dockerfile adds a script called updateConfig.sh to the image. That script is executed before the server is started and (among other things) updates the hostnames in serverindex.xml when necessary.

Docker expects the RUN command to run the server process in the foreground (instead of allowing it to detach) and to gracefully stop the server when receiving a TERM signal. WebSphere's startServer.sh command doesn't meet these requirements. This issue is solved by using the -script option, which tells startServer.sh to generate a launch script instead of starting the server. This launch script has the desired properties and is used by the RUN command. This has an additional benefit: the startServer.sh command itself takes a significant amount of time (it's a Java process that reads the configuration and then starts a separate process for the actual WebSphere server) and skipping it reduces the startup time.

There is however a problem with this approach. The content of the launch script generated by startServer.sh depends on the server configuration, in particular the JVM settings specified in server.xml. When they change, the launch script needs to be regenerated. This can be easily detected and the updateConfig.sh script added by the Dockerfile is designed to take care of this.

The RUN command is a script that first runs updateConfig.sh and then executes the launch script. In addition to that, updateConfig.sh is also executed once during the image creation. This will speed up the first start of a new container created from that image, not only because the launch script will already exist, but also because the very first execution of the startServer.sh script typically takes much longer to complete.

Sunday, October 12, 2014

The StAX API uses the so called JDK 1.3 service provider discovery mechanism to locate providers of its three factory classes (XMLInputFactory, XMLOutputFactory and XMLEventFactory). This mechanism uses the thread context class loader to look up resources under META-INF/services/. Switching to a StAX implementation other than the one shipped with the JRE therefore requires deploying the JAR containing that implementation in such a way that it becomes visible to the thread context class loader.

In a simple J2SE application, the thread context class loader is set to the application class loader and the right way to do this is to add the JAR to the class path. In a JavaEE environment, each application (EAR) and each Web module (WAR) has its own class loader and the specification requires that the application server sets the thread context class loader to the corresponding application or module class loader before invoking a component (servlet, bean, etc.). To change the StAX implementation used by a given application or module, it is therefore enough to add the JAR to the relevant EAR or WAR.

Things are different in an OSGi environment because each bundle has its own class loader, but the thread context class loader is undefined. JDK 1.3 service provider discovery will therefore not be able to discover a StAX implementation deployed as a bundle. The solution to this problem is to modify the StAX API to replace the JDK 1.3 service provider discovery with an OSGi aware mechanism not relying on the thread context class loader. That modified StAX API would then be deployed as an OSGi bundle itself so that it will be used in place of the StAX API from the JRE. At least two such StAX API bundles exist: one from the Apache Geronimo project and one from Apache ServiceMix.

In the following we will discuss how they work, and in particular how they can be used to switch to Woodstox as StAX implementation. Note that the Woodstox JAR (Maven: org.codehaus.woodstox:woodstox-core-asl) as well as the StAX2 API JAR (Maven: org.codehaus.woodstox:stax2-api) on which it depends already have OSGi manifests and therefore can be deployed as bundles without the need to repackage them.

To use the StAX support from Apache Geronimo, two bundles need to be installed: the Geronimo OSGi registry (Maven: org.apache.geronimo.specs:geronimo-osgi-registry:1.1) and the Geronimo StAX API bundle (Maven: org.apache.geronimo.specs:geronimo-stax-api_1.2_spec:1.2). The OSGi registry tracks bundles that have the SPI-Provider attribute set to true in their manifests. It scans these bundles for resources under META-INF/services/. That information is then used by the StAX API bundle to locate the StAX implementation. This means that Geronimo uses the same metadata as the JDK 1.3 service provider discovery, but requires an additional (non standard) bundle manifest attribute. The stock Woodstox bundle doesn't have this attribute and therefore will not be recognized. Instead, a repackaged version of Woodstox is required. The Geronimo project provides this kind of bundles (Maven: org.apache.geronimo.bundles:woodstox-core-asl), albeit not for the most recent Woodstox versions.

The StAX support from Apache ServiceMix comes as a single bundle to deploy (Maven: org.apache.servicemix.specs:org.apache.servicemix.specs.stax-api-1.2:2.4.0). It scans all bundles for StAX related resources under META-INF/services/, i.e. it uses exactly the same metadata as the JDK 1.3 service provider discovery. This means that it will recognize the vanilla Woodstox bundle and no repackaging is required.

To summarize, the most effective way to switch to Woodstox as the StAX implementation in an OSGi environment is to deploy the following three bundles (identified by their Maven coordinates):

Saturday, February 15, 2014

The other day I came across a very interesting deadlock situation in an application deployed on a production WebSphere server. The deadlock occurred because for certain requests, the application requires more than one concurrent connection from the same JDBC data source in a single thread. This situation arises e.g. when an application uses transaction suspension (e.g. by calling an EJB method declared with REQUIRES_NEW) and the new transaction uses a data source that has already been accessed in the suspended transaction. In that case, the container is required to retrieve a new connection from the pool, resulting in two connections from the same data source being held by the same thread at the same time. Since the size of the connection pool is bounded, this may indeed lead to a deadlock if multiple such requests are processed concurrently. There is very well written explanation of that problem in the WebSphere documentation:

Deadlock can occur if the application requires more than one concurrent connection per thread, and the database connection pool is not large enough for the number of threads. Suppose each of the application threads requires two concurrent database connections and the number of threads is equal to the maximum connection pool size. Deadlock can occur when both of the following conditions are true:

Each thread has its first database connection, and all are in use.

Each thread is waiting for a second database connection, and none would become available since all threads are blocked.

To prevent the deadlock in this case, increase the maximum connections value for the database connection pool by at least one. This ensures that at least one of the waiting threads obtains a second database connection and avoids a deadlock scenario.

For general prevention of connection deadlock, code your applications to use only one connection per thread. If you code the application to require C concurrent database connections per thread, the connection pool must support at least the following number of connections, where T is the maximum number of threads:

T * (C - 1) + 1

The deadlock situation can be visualized using a resource allocation diagram. With 4 threads, a maximum connection pool size of 4 and C=2, the diagram would look as follows:

Note that since blocked connection requests eventually time out (by default after 3 minutes), the situation is not a real (permanent) deadlock. However, after a given thread is unblocked by a timeout (and the connection held by that thread released), the system will typically reach another deadlock state very quickly because of application requests that have been queued in the meantime (by the Web container if requests come in via HTTP).

What makes this problem so nasty is that it is a threshold phenomenon. Under increasing load the system will at first behave gently: as long as the maximum pool size is not reached, it is not possible for the deadlock to occur and the system will respond in a normal way. If the load increases further, the number of active connections will eventually reach the limit and the probability for the deadlock to occur will become non zero. Once the deadlock materializes, the behavior of the system drastically changes, and the impact is not limited to requests that require multiple concurrent connections per thread: any request depending on the data source (even with C=1) will be blocked. This will rapidly lead to a thread pool starvation, blocking all incoming requests, even ones that don't use the data source. As noted above, connection request timeouts will not necessarily improve the situation, even if the load (in terms of number of incoming requests per unit of time) decreases below the level that initially triggered the deadlock.

To illustrate the last point, assume that the normal response time of the service is of order 100ms and that the maximum connection pool size is 10. In this scenario the threshold above which the deadlock may occur is of order 100 req/s. Once the deadlock occurs, the average response time drastically changes. It will be determined by the connection request timeout configured on the data source, which is 3 minutes by default. The actual average response time will be lower because once a timeout occurs and a connection becomes available in the pool, a certain number of requests may go through without triggering the deadlock again. Let's be optimistic and assume that in that state the average response time will be of order 10 seconds. Then the new threshold will be of order 1 req/s, i.e. for the deadlock to clear there would have to be a drastic decrease in load.

As noted in the WebSphere documentation quoted above, there are two options to avoid the problem. One is to set the maximum pool size for the data source to a sufficiently high value. Note that as long as there are requests with C>1, the maximum connection pool size must be larger than the thread pool size. There are some problems with this option:

In many environments there is a limit on the total number of open connections allowed by the database. Configuring large connection pools may cause a problem at that level.

Increasing connection pool sizes also increases the maximum number of SQL statements that may be executed concurrently. This may cause problems for the database server in other scenarios.

The other option is to review the application and to make sure that C≤1 for all requests. This raises another interesting question, namely how to identify code for which C>1 without the need to carry out specific load tests that attempt to trigger the actual deadlock or to implement costly code reviews (that would probably miss some scenarios anyway). Ideally one would like to identify such code by simply monitoring the application in a test environment. In principle this should be feasible because the algorithm to detect this at runtime is trivial: if a thread requests a new connection from a pool while it already owns one, take a stack trace and log the event.

It appears that WebSphere Application Server doesn't have any feature that would allow to do that. On the other hand this is a typical use case for tools such as BTrace. Unfortunately BTrace is known not to work on IBM JREs because it uses an undocumented feature that only exists in Oracle JREs. There is however a similar tool called Byteman that works on IBM JREs.

The following Byteman script indeed achieves the goal (Note that it was written for WAS 8.5; it may need some changes to work on earlier versions):

The script simply intercepts the relevant calls to the connection pool manager that are used for reserving and releasing connections. It then extracts the MCWrapper object (MC stands for managed connection; there is one wrapper for each physical connection in the pool) and passes it to a helper class that takes care of the bookkeeping. If the helper detects that two wrappers from the same pool are used by a single thread, it will log that event. The class looks as follows:

package helper;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;
public class Helper {
// Note: key is PoolManager and values are MCWrapper. We need to use Object because the helper is
// added to the classpath of the server. If we want to use the actual classes, then we would have
// to load the helper as a fragment into the com.ibm.ws.runtime bundle.
private static final ThreadLocal<Map<Object,Set<Object>>> threadLocal
= new ThreadLocal<Map<Object,Set<Object>>>() {
@Override
protected Map<Object,Set<Object>> initialValue() {
return new HashMap<Object,Set<Object>>();
}
};
public void reserved(Object poolManager, Object mcWrapper) {
Map<Object,Set<Object>> map = threadLocal.get();
Set<Object> mcWrappers = map.get(poolManager);
if (mcWrappers == null) {
mcWrappers = new HashSet<Object>();
map.put(poolManager, mcWrappers);
}
// Note that the same MCWrapper may be returned twice if the connection is sharable and requested
// multiple times in the same transaction (which is OK); however we don't need to track that
// because "released" is only called once per MCWrapper.
mcWrappers.add(mcWrapper);
if (mcWrappers.size() > 1) {
System.out.println("Detected concurrent connection requests for the same pool in the same thread!");
System.out.println(poolManager);
new Throwable().printStackTrace(System.out);
}
}
public void released(Object poolManager, Object mcWrapper) {
// Note that this method is called only once per MCWrapper for shared connections (i.e. when
// the MCWrapper is really put back into the pool).
threadLocal.get().get(poolManager).remove(mcWrapper);
}
}

That class needs to be added to the class path of the server. The Byteman script itself is enabled by adding the following argument to the JVM command line of the WebSphere server:

When the detection mechanism is triggered, it will output a dump of the connection pool as well as a stack trace for the code that requests the concurrent connection. The connection pool dump will show at least one connection with a managed connection wrapped linked to a transaction in state SUSPENDED. It is easy to improve the helper class to collect the stack trace for the first connection request as well. Note however that this changes requires the helper to save a stack trace for every connection request (even for code with C=1) which would have an impact on performance.

Wednesday, February 5, 2014

In a previous blog post I discussed a couple of common pitfalls when using HADR and automatic client reroute with DB2 and WebSphere. In the present post I will analyze another closely related topic, namely how WebSphere and applications deployed on WebSphere react to a client reroute and what can be done to minimize the impact of a failover.

There are a couple of things one needs to be aware of in order to analyze these questions:

The failover of a database always causes all active transactions on that database to be rolled back. The fundamental reason is that HADR doesn’t replicate locks to the standby database, as mentioned here. Note that, on the other hand, HADR does ship log records for uncommitted operations (which means that transactions that are rolled back on the primary also cause a roll back on the standby). The standby therefore has enough information to reconstruct the data in an active transaction, but the fact that locks are not replicated implies that it cannot fully reconstruct the state of the active transactions during a failover. It therefore cannot allow these transactions to continue and is forced to perform a rollback.

By default, when the JDBC driver performs a client reroute after detecting that a database has failed over, it will trigger a com.ibm.db2.jcc.am.ClientRerouteException (with ERRORCODE=-4498 and SQLSTATE=08506). This exception will be mapped by WebSphere to a com.ibm.websphere.ce.cm.StaleConnectionException before it is received by the application.

Note that this occurs during the first attempt to reuse an existing connection after the failover. Since connections are pooled, there may be a significant delay between the failover and the occurrence of the ClientRerouteException/StaleConnectionException.

The correct way to react to a ClientRerouteException/StaleConnectionException would therefore be to reexecute the entire transaction. Obviously there is a special case, namely a reroute occurring while attempting to execute the first query in a transaction. In this situation, only a single operation needs to be reexecuted. Note that this is actually the most common case because it occurs for transactions started after the failover, but that attempt to reuse a connection established before the failover. Typically this is more likely than a failover in the middle of a transaction (except of course on very busy systems or applications that use long running transactions).

The JDBC data source can be configured to automatically handle that special case. This feature is called seamless failover. The DB2 documentation describes the conditions that need to be satisfied for seamless failover to be effective:

If seamless failover is enabled, the driver retries the transaction on the new server, without notifying the application.

The following conditions must be satisfied for seamless failover to occur:

The enableSeamlessFailover property is set to DB2BaseDataSource.YES. [...]

The connection is not in a transaction. That is, the failure occurs when the first SQL statement in the transaction is executed.

There are no global temporary tables in use on the server.

There are no open, held cursors.

This still leaves the case where the failover occurs in the middle of a transaction. The DB2 documentation has an example that shows how an application could react in this situation by reexecuting the entire transaction. However, the approach suggested by that example is not realistic for real world applications. There are multiple reasons for that:

It requires lot of boilerplate error handling code to be added to the application. That code would be much more complex than what is suggested by the example. Just to name a few complications that may occur: reuse of the same data access code in different transactions, container managed transactions, distributed transactions, the option to join an existing transaction, transactions started by and imported from remote clients, etc.

Writing and maintaining that code is very error-prone. It is very easy to get it wrong, so that instead of reexecuting the current transaction, the code would only partially reexecute the transaction or reexecute queries that are part of a previous transaction that has already been committed. Since the code is not executed during normal program flow, such bugs will not be noticed immediately.

It is virtually impossible to test this code. One would have to find a way to trigger or simulate a database failover at a well defined moment during code execution. One would then have to apply this technique to every possible partially executed transaction that can occur in the application. This is simply not realistic.

A more realistic option would be to handle this at the framework level. E.g. it is likely that Spring could be set up or extended to support automatic transaction reexecution in case of a client reroute. If this support is designed carefully and tested thoroughly, then one can reasonably assume that it just works transparently for any transaction, removing the need to test it individually for every transaction.

However, before embarking on this endeavor, you should ask yourself if the return on investment is actually high enough. You should take into account the following aspects in your evaluation:

There may be multiple frameworks in use in your organization (e.g. EJB and Spring). Automatic transaction reexecution would have to be implemented for each of these frameworks separately. For some frameworks, it may be impossible to implement this in a way that is transparent for applications.

Database failovers are expected to be rare events. If seamless failover is enabled, then only transactions that are active at the time of the failover will be impacted. This means that the failure rate may be very low.

When the primary DB2 instance goes down because of a crash, it will take some time before the standby takes over. Even if the application successfully reexecutes the transaction, the client of the application may still receive an error because of timeouts. On the other hand, in case of a manual takeover for maintenance reasons, one can usually reduce the impact on clients by carefully scheduling the takeover.

There are lots of reasons why a client request may fail, and database failovers are only one possible cause. Other causes include application server crashes and network issues. It is likely that implementing automatic transaction reexecution would reduce the overall failure rate only marginally. It may actually be more interesting to implement a mechanism that retries requests on the client side for any kind of failure.

Message driven beans already provide a retry mechanism that is transactionally safe. In some cases this may be a better option than implementing a custom solution.

The conclusion is that while it is in general a good idea to enable seamless failover, in most cases it is not worth trying to intercept ClientRerouteException/StaleConnectionException and to automatically reexecute transactions.